In [78]:

full_data.head(10)

Out[78]:

	Country,Other	TotalCases	NewCases	TotalDeaths	NewDeaths	TotalRecovered	ActiveCases	Serious,Critical	Tot Cases/1M pop	Deaths/1M pop	TotalTests	Tests/ 1M pop
0	World	3954826	+41,182	271995.0	+1,569	1361994.0	2320837	49015.0	507.0	34.9	NaN	NaN
1	USA	1296879	+4,256	77190.0	+262	218833.0	1000856	16985.0	3918.0	233.0	8364844.0	25271.0
2	Spain	260117	+3,262	26299.0	+229	168408.0	65410	2075.0	5563.0	562.0	1932455.0	41332.0
3	Italy	215858	NaN	29958.0	NaN	96276.0	89624	1311.0	3570.0	495.0	2381288.0	39385.0
4	UK	206715	NaN	30615.0	NaN	NaN	175756	1559.0	3045.0	451.0	1534533.0	22605.0
5	Russia	187859	+10,699	1723.0	+98	26608.0	159528	2300.0	1287.0	12.0	4980000.0	34125.0
6	France	174791	NaN	25987.0	NaN	55027.0	93777	2961.0	2678.0	398.0	1384633.0	21213.0
7	Germany	169430	NaN	7392.0	NaN	141700.0	20338	1823.0	2022.0	88.0	2755770.0	32891.0
8	Brazil	137309	+1,616	9307.0	+119	55350.0	72652	8318.0	646.0	44.0	339552.0	1597.0
9	Turkey	133721	NaN	3641.0	NaN	82984.0	47096	1260.0	1586.0	43.0	1265119.0	15000.0

In [84]:

covid_time_series_C= pd.read_csv('time_series_covid19_confirmed.csv')
covid_time_series_C

Out[84]:

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	4/27/20	4/28/20	4/29/20	4/30/20	5/1/20	5/2/20	5/3/20	5/4/20	5/5/20	5/6/20
0	NaN	Afghanistan	33.000000	65.000000	0	0	0	0	0	0	...	1703	1828	1939	2171	2335	2469	2704	2894	3224	3392
1	NaN	Albania	41.153300	20.168300	0	0	0	0	0	0	...	736	750	766	773	782	789	795	803	820	832
2	NaN	Algeria	28.033900	1.659600	0	0	0	0	0	0	...	3517	3649	3848	4006	4154	4295	4474	4648	4838	4997
3	NaN	Andorra	42.506300	1.521800	0	0	0	0	0	0	...	743	743	743	745	745	747	748	750	751	751
4	NaN	Angola	-11.202700	17.873900	0	0	0	0	0	0	...	27	27	27	27	30	35	35	35	36	36
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
261	NaN	Western Sahara	24.215500	-12.885800	0	0	0	0	0	0	...	6	6	6	6	6	6	6	6	6	6
262	NaN	Sao Tome and Principe	0.186360	6.613081	0	0	0	0	0	0	...	4	8	8	14	16	16	16	23	174	174
263	NaN	Yemen	15.552727	48.516388	0	0	0	0	0	0	...	1	1	6	6	7	10	10	12	22	25
264	NaN	Comoros	-11.645500	43.333300	0	0	0	0	0	0	...	0	0	0	1	1	3	3	3	3	8
265	NaN	Tajikistan	38.861034	71.276093	0	0	0	0	0	0	...	0	0	0	15	15	76	128	230	293	379

266 rows × 110 columns

In [85]:

covid_time_series_covid_19_R = pd.read_csv('time_series_covid19_recovered.csv')
covid_time_series_covid_19_R

Out[85]:

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	4/27/20	4/28/20	4/29/20	4/30/20	5/1/20	5/2/20	5/3/20	5/4/20	5/5/20	5/6/20
0	NaN	Afghanistan	33.000000	65.000000	0	0	0	0	0	0	...	220	228	252	260	310	331	345	397	421	458
1	NaN	Albania	41.153300	20.168300	0	0	0	0	0	0	...	422	431	455	470	488	519	531	543	570	595
2	NaN	Algeria	28.033900	1.659600	0	0	0	0	0	0	...	1558	1651	1702	1779	1821	1872	1936	1998	2067	2197
3	NaN	Andorra	42.506300	1.521800	0	0	0	0	0	0	...	385	398	423	468	468	472	493	499	514	521
4	NaN	Angola	-11.202700	17.873900	0	0	0	0	0	0	...	6	6	7	7	11	11	11	11	11	11
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
247	NaN	Western Sahara	24.215500	-12.885800	0	0	0	0	0	0	...	5	5	5	5	5	5	5	5	5	5
248	NaN	Sao Tome and Principe	0.186360	6.613081	0	0	0	0	0	0	...	0	4	4	4	4	4	4	4	4	4
249	NaN	Yemen	15.552727	48.516388	0	0	0	0	0	0	...	1	1	1	1	1	1	1	1	1	1
250	NaN	Comoros	-11.645500	43.333300	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
251	NaN	Tajikistan	38.861034	71.276093	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

252 rows × 110 columns

In [86]:

covid_time_series_D = pd.read_csv('time_series_covid19_deaths.csv')
covid_time_series_D

Out[86]:

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	4/27/20	4/28/20	4/29/20	4/30/20	5/1/20	5/2/20	5/3/20	5/4/20	5/5/20	5/6/20
0	NaN	Afghanistan	33.000000	65.000000	0	0	0	0	0	0	...	57	58	60	64	68	72	85	90	95	104
1	NaN	Albania	41.153300	20.168300	0	0	0	0	0	0	...	28	30	30	31	31	31	31	31	31	31
2	NaN	Algeria	28.033900	1.659600	0	0	0	0	0	0	...	432	437	444	450	453	459	463	465	470	476
3	NaN	Andorra	42.506300	1.521800	0	0	0	0	0	0	...	40	41	42	42	43	44	45	45	46	46
4	NaN	Angola	-11.202700	17.873900	0	0	0	0	0	0	...	2	2	2	2	2	2	2	2	2	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
261	NaN	Western Sahara	24.215500	-12.885800	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
262	NaN	Sao Tome and Principe	0.186360	6.613081	0	0	0	0	0	0	...	0	0	0	0	1	1	1	3	3	3
263	NaN	Yemen	15.552727	48.516388	0	0	0	0	0	0	...	0	0	0	2	2	2	2	2	4	5
264	NaN	Comoros	-11.645500	43.333300	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
265	NaN	Tajikistan	38.861034	71.276093	0	0	0	0	0	0	...	0	0	0	0	0	2	2	3	5	8

266 rows × 110 columns

In [92]:

full_data['new_cases'] = full_data['new_cases'].str.replace(',', '', regex=True)
full_data['new_deaths'] = full_data['new_deaths'].str.replace(',','', regex=True)
full_data['new_cases'].fillna(0,inplace=True)
full_data['new_deaths'].fillna(0,inplace=True)
full_data['new_cases'] = full_data['new_cases'].astype('int64')
full_data['new_deaths'] = full_data['new_deaths'].astype('int64')
full_data

Out[92]:

	country	total_cases	new_cases	total_deaths	new_deaths	total_recovered	active_cases	serious	tot cases/1m_pop	deaths/1m_pop	total_tests	tests/_1m_pop
0	World	3954826	41182	271995	1569	1361994	2320837	49015	507.0	34	0	0
1	USA	1296879	4256	77190	262	218833	1000856	16985	3918.0	233	8364844	25271
2	Spain	260117	3262	26299	229	168408	65410	2075	5563.0	562	1932455	41332
3	Italy	215858	0	29958	0	96276	89624	1311	3570.0	495	2381288	39385
4	UK	206715	0	30615	0	0	175756	1559	3045.0	451	1534533	22605
...	...	...	...	...	...	...	...	...	...	...	...	...
211	Western Sahara	6	0	0	0	5	1	0	10.0	0	0	0
212	Anguilla	3	0	0	0	3	0	0	200.0	0	0	0
213	Saint Pierre Miquelon	1	0	0	0	0	1	0	173.0	0	0	0
214	China	82886	1	4633	0	77993	260	18	58.0	3	0	0
215	Total:	3954826	41182	271995	1569	1361994	2320837	49015	507.4	34	0	0

216 rows × 12 columns

In [94]:

full_data.describe()

Out[94]:

	total_cases	new_cases	total_deaths	new_deaths	total_recovered	active_cases	serious	tot cases/1m_pop	deaths/1m_pop	total_tests	tests/_1m_pop
count	2.140000e+02	214.000000	214.000000	214.000000	214.000000	2.140000e+02	214.000000	214.000000	214.000000	2.140000e+02	214.000000
mean	1.848050e+04	192.439252	1271.004673	7.331776	6361.682243	1.084503e+04	229.042056	963.083645	44.378505	2.053834e+05	14729.014019
std	9.549323e+04	866.054506	6571.131172	33.503657	24497.834896	7.098710e+04	1342.064653	2075.474604	131.409269	7.442097e+05	25822.167160
min	1.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000e+00	0.000000
25%	9.325000e+01	0.000000	2.000000	0.000000	27.000000	2.600000e+01	0.000000	42.250000	0.000000	8.067500e+02	418.250000
50%	7.230000e+02	0.000000	13.000000	0.000000	246.500000	2.785000e+02	2.500000	198.500000	3.000000	1.373200e+04	3521.500000
75%	5.323750e+03	28.000000	108.750000	0.750000	1749.750000	2.294500e+03	21.750000	1032.500000	24.000000	1.267548e+05	17848.250000
max	1.296879e+06	10699.000000	77190.000000	262.000000	218833.000000	1.000856e+06	16985.000000	18361.000000	1208.000000	8.364844e+06	171971.000000

In [95]:

full_data.sum()

Out[95]:

country             USASpainItalyUKRussiaFranceGermanyBrazilTurkey...
total_cases                                                   3954826
new_cases                                                       41182
total_deaths                                                   271995
new_deaths                                                       1569
total_recovered                                               1361400
active_cases                                                  2320837
serious                                                         49015
tot cases/1m_pop                                               206100
deaths/1m_pop                                                    9497
total_tests                                                  43952042
tests/_1m_pop                                                 3152009
dtype: object

In [110]:

covid_india.describe()

Out[110]:

	Sno	Cured	Deaths	Confirmed
count	1671.000000	1671.000000	1671.000000	1671.000000
mean	836.000000	86.929982	13.054458	408.981448
std	482.520466	251.026352	49.430082	1188.132537
min	1.000000	0.000000	0.000000	0.000000
25%	418.500000	0.000000	0.000000	5.000000
50%	836.000000	5.000000	0.000000	32.000000
75%	1253.500000	34.000000	4.000000	258.500000
max	1671.000000	2819.000000	617.000000	15525.000000

In [126]:

covid_time_series_C.describe()

Out[126]:

	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	1/28/20	1/29/20	...	4/27/20	4/28/20	4/29/20	4/30/20	5/1/20	5/2/20	5/3/20	5/4/20	5/5/20	5/6/20
count	266.000000	266.000000	266.000000	266.000000	266.000000	266.000000	266.000000	266.000000	266.000000	266.000000	...	266.000000	2.660000e+02	2.660000e+02	2.660000e+02	2.660000e+02	2.660000e+02	2.660000e+02	2.660000e+02	2.660000e+02	2.660000e+02
mean	21.259359	22.432499	2.086466	2.458647	3.537594	5.390977	7.962406	11.003759	20.969925	23.180451	...	11367.375940	1.164357e+04	1.192589e+04	1.224381e+04	1.257059e+04	1.288475e+04	1.318319e+04	1.347013e+04	1.376952e+04	1.411782e+04
std	24.747943	70.478908	27.279200	27.377862	34.083035	47.434934	66.289178	89.313757	219.187744	220.524977	...	65963.451143	6.750722e+04	6.918850e+04	7.102979e+04	7.311436e+04	7.495796e+04	7.657327e+04	7.801568e+04	7.957189e+04	8.121537e+04
min	-51.796300	-135.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
25%	6.907750	-18.093125	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	74.250000	7.500000e+01	7.600000e+01	7.725000e+01	8.100000e+01	8.200000e+01	8.225000e+01	8.675000e+01	9.350000e+01	9.525000e+01
50%	23.488100	20.921188	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	423.500000	4.335000e+02	4.555000e+02	4.665000e+02	4.855000e+02	4.995000e+02	5.085000e+02	5.425000e+02	5.480000e+02	5.560000e+02
75%	41.143200	77.191525	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	1974.500000	2.020000e+03	2.047250e+03	2.105250e+03	2.165750e+03	2.359250e+03	2.515000e+03	2.635750e+03	2.699750e+03	2.872750e+03
max	71.706900	178.065000	444.000000	444.000000	549.000000	761.000000	1058.000000	1423.000000	3554.000000	3554.000000	...	988197.000000	1.012582e+06	1.039909e+06	1.069424e+06	1.103461e+06	1.132539e+06	1.158040e+06	1.180375e+06	1.204351e+06	1.228603e+06

8 rows × 108 columns

In [127]:

covid_time_series_covid_19_R.describe()

Out[127]:

	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	1/28/20	1/29/20	...	4/27/20	4/28/20	4/29/20	4/30/20	5/1/20	5/2/20	5/3/20	5/4/20	5/5/20	5/6/20
count	252.000000	252.000000	252.000000	252.000000	252.000000	252.000000	252.000000	252.000000	252.000000	252.000000	...	252.000000	252.000000	252.000000	252.000000	252.000000	252.000000	252.000000	252.000000	252.000000	252.000000
mean	19.997457	28.167963	0.111111	0.119048	0.142857	0.154762	0.206349	0.242063	0.424603	0.500000	...	3466.972222	3598.980159	3763.591270	4023.297619	4176.250000	4337.746032	4465.222222	4613.984127	4757.269841	4942.115079
std	24.408240	67.225277	1.763834	1.767827	1.958592	2.024712	2.654973	2.858017	5.069858	5.577059	...	14409.367219	14829.617179	15399.747262	16859.803967	17468.700495	18210.920341	18611.712422	19134.155254	19528.619764	20013.875983
min	-51.796300	-106.346800	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	6.565350	-7.825200	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	18.000000	19.000000	19.750000	24.750000	25.750000	25.750000	27.000000	28.500000	30.000000	30.000000
50%	21.805100	23.409400	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	170.000000	182.000000	182.000000	198.000000	209.500000	213.000000	226.000000	232.000000	242.000000	256.500000
75%	39.329025	85.953175	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	927.500000	977.500000	1007.500000	1022.000000	1083.250000	1156.500000	1224.000000	1275.750000	1317.250000	1342.500000
max	71.706900	178.065000	28.000000	28.000000	31.000000	32.000000	42.000000	45.000000	80.000000	88.000000	...	114500.000000	117400.000000	120720.000000	153947.000000	164015.000000	175382.000000	180152.000000	187180.000000	189791.000000	189910.000000

8 rows × 108 columns

In [128]:

covid_time_series_D.describe()

Out[128]:

	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	1/28/20	1/29/20	...	4/27/20	4/28/20	4/29/20	4/30/20	5/1/20	5/2/20	5/3/20	5/4/20	5/5/20	5/6/20
count	266.000000	266.000000	266.000000	266.000000	266.000000	266.000000	266.000000	266.000000	266.000000	266.000000	...	266.000000	266.000000	266.000000	266.000000	266.000000	266.000000	266.000000	266.000000	266.000000	266.000000
mean	21.259359	22.432499	0.063910	0.067669	0.097744	0.157895	0.210526	0.308271	0.492481	0.500000	...	806.180451	830.071429	855.883459	877.281955	897.063910	916.571429	930.338346	945.627820	967.063910	991.845865
std	24.747943	70.478908	1.042337	1.043908	1.473615	2.453621	3.189730	4.660845	7.664297	7.664793	...	4605.196763	4744.311185	4906.661988	5033.525489	5151.692272	5255.657646	5334.737320	5413.828455	5546.438359	5694.709721
min	-51.796300	-135.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	6.907750	-18.093125	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
50%	23.488100	20.921188	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	7.000000	7.000000	7.000000	8.000000	8.000000	8.000000	9.000000	9.000000	9.000000	9.000000
75%	41.143200	77.191525	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	56.750000	58.000000	59.750000	61.000000	67.500000	70.500000	75.250000	78.000000	79.750000	85.750000
max	71.706900	178.065000	17.000000	17.000000	24.000000	40.000000	52.000000	76.000000	125.000000	125.000000	...	56219.000000	58355.000000	60967.000000	62996.000000	64943.000000	66369.000000	67682.000000	68922.000000	71064.000000	73431.000000

8 rows × 108 columns

In [134]:

#covid_time_series_covid_19_R = covid_time_series_covid_19_R.drop(['Province/State','Lat','Long'], axis=1)
covid_time_series_covid_19_R = covid_time_series_covid_19_R.drop(['Lat','Long'], axis=1)

covid_time_series_covid_19_R

Out[134]:

	Province/State	Country/Region	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	1/28/20	1/29/20	...	4/27/20	4/28/20	4/29/20	4/30/20	5/1/20	5/2/20	5/3/20	5/4/20	5/5/20	5/6/20
0	0	Afghanistan	0	0	0	0	0	0	0	0	...	220	228	252	260	310	331	345	397	421	458
1	0	Albania	0	0	0	0	0	0	0	0	...	422	431	455	470	488	519	531	543	570	595
2	0	Algeria	0	0	0	0	0	0	0	0	...	1558	1651	1702	1779	1821	1872	1936	1998	2067	2197
3	0	Andorra	0	0	0	0	0	0	0	0	...	385	398	423	468	468	472	493	499	514	521
4	0	Angola	0	0	0	0	0	0	0	0	...	6	6	7	7	11	11	11	11	11	11
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
247	0	Western Sahara	0	0	0	0	0	0	0	0	...	5	5	5	5	5	5	5	5	5	5
248	0	Sao Tome and Principe	0	0	0	0	0	0	0	0	...	0	4	4	4	4	4	4	4	4	4
249	0	Yemen	0	0	0	0	0	0	0	0	...	1	1	1	1	1	1	1	1	1	1
250	0	Comoros	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
251	0	Tajikistan	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

252 rows × 108 columns

In [143]:

#covid_time_seriesChina = covid_time_series[covid_time_series['Country/Region'] == 'China']
#covid_time_seriesChina.head(50)
covid_time_series

Out[143]:

	Country/Region	Date	Confirmed	Deaths	Recovered	Death_percentage	Recovered_percentage
0	Afghanistan	1/23/20	0	0	0	NaN	NaN
1	Afghanistan	1/24/20	0	0	0	NaN	NaN
2	Afghanistan	1/25/20	0	0	0	NaN	NaN
3	Afghanistan	1/26/20	0	0	0	NaN	NaN
4	Afghanistan	1/27/20	0	0	0	NaN	NaN
...	...	...	...	...	...	...	...
19630	Zimbabwe	5/2/20	34	4	5	0.001176	0.001471
19631	Zimbabwe	5/3/20	34	4	5	0.001176	0.001471
19632	Zimbabwe	5/4/20	34	4	5	0.001176	0.001471
19633	Zimbabwe	5/5/20	34	4	5	0.001176	0.001471
19634	Zimbabwe	5/6/20	34	4	5	0.001176	0.001471

19635 rows × 7 columns

In [149]:

covid_time_series.head(50)

Out[149]:

	Country/Region	Date	Confirmed	Recovered	Death_percentage	Recovered_percentage	Day
0	Afghanistan	1/23/20	0	0	NaN	NaN	0
1	Afghanistan	1/24/20	0	0	NaN	NaN	0
2	Afghanistan	1/25/20	0	0	NaN	NaN	0
3	Afghanistan	1/26/20	0	0	NaN	NaN	0
4	Afghanistan	1/27/20	0	0	NaN	NaN	0
5	Afghanistan	1/28/20	0	0	NaN	NaN	0
6	Afghanistan	1/29/20	0	0	NaN	NaN	0
7	Afghanistan	1/30/20	0	0	NaN	NaN	0
8	Afghanistan	1/31/20	0	0	NaN	NaN	0
9	Afghanistan	2/1/20	0	0	NaN	NaN	0
10	Afghanistan	2/10/20	0	0	NaN	NaN	0
11	Afghanistan	2/11/20	0	0	NaN	NaN	0
12	Afghanistan	2/12/20	0	0	NaN	NaN	0
13	Afghanistan	2/13/20	0	0	NaN	NaN	0
14	Afghanistan	2/14/20	0	0	NaN	NaN	0
15	Afghanistan	2/15/20	0	0	NaN	NaN	0
16	Afghanistan	2/16/20	0	0	NaN	NaN	0
17	Afghanistan	2/17/20	0	0	NaN	NaN	0
18	Afghanistan	2/18/20	0	0	NaN	NaN	0
19	Afghanistan	2/19/20	0	0	NaN	NaN	0
20	Afghanistan	2/2/20	0	0	NaN	NaN	0
21	Afghanistan	2/20/20	0	0	NaN	NaN	0
22	Afghanistan	2/21/20	0	0	NaN	NaN	0
23	Afghanistan	2/22/20	0	0	NaN	NaN	0
24	Afghanistan	2/23/20	0	0	NaN	NaN	0
25	Afghanistan	2/24/20	1	0	0.0	0.000000	0
26	Afghanistan	2/25/20	1	0	0.0	0.000000	1
27	Afghanistan	2/26/20	1	0	0.0	0.000000	2
28	Afghanistan	2/27/20	1	0	0.0	0.000000	3
29	Afghanistan	2/28/20	1	0	0.0	0.000000	4
30	Afghanistan	2/29/20	1	0	0.0	0.000000	5
31	Afghanistan	2/3/20	0	0	NaN	NaN	0
32	Afghanistan	2/4/20	0	0	NaN	NaN	0
33	Afghanistan	2/5/20	0	0	NaN	NaN	0
34	Afghanistan	2/6/20	0	0	NaN	NaN	0
35	Afghanistan	2/7/20	0	0	NaN	NaN	0
36	Afghanistan	2/8/20	0	0	NaN	NaN	0
37	Afghanistan	2/9/20	0	0	NaN	NaN	0
38	Afghanistan	3/1/20	1	0	0.0	0.000000	6
39	Afghanistan	3/10/20	5	0	0.0	0.000000	15
40	Afghanistan	3/11/20	7	0	0.0	0.000000	16
41	Afghanistan	3/12/20	7	0	0.0	0.000000	17
42	Afghanistan	3/13/20	7	0	0.0	0.000000	18
43	Afghanistan	3/14/20	11	0	0.0	0.000000	19
44	Afghanistan	3/15/20	16	0	0.0	0.000000	20
45	Afghanistan	3/16/20	21	1	0.0	0.000476	21
46	Afghanistan	3/17/20	22	1	0.0	0.000455	22
47	Afghanistan	3/18/20	22	1	0.0	0.000455	23
48	Afghanistan	3/19/20	22	1	0.0	0.000455	24
49	Afghanistan	3/2/20	1	0	0.0	0.000000	7

In [156]:

X = full_data[['total_deaths','new_cases','new_deaths']] 
# #### fit a OLS model with intercept on total_cases and  new_cases,new_deaths.
y = full_data['total_cases']
X = sm.add_constant(X)
est = sm.OLS(y, X).fit()
print(est.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:            total_cases   R-squared:                       0.905
Model:                            OLS   Adj. R-squared:                  0.903
Method:                 Least Squares   F-statistic:                     665.7
Date:                Fri, 08 May 2020   Prob (F-statistic):          5.97e-107
Time:                        21:49:36   Log-Likelihood:                -2505.4
No. Observations:                 214   AIC:                             5019.
Df Residuals:                     210   BIC:                             5032.
Df Model:                           3                                         
Covariance Type:            nonrobust                                         
================================================================================
                   coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------
const        -1373.8449   2095.087     -0.656      0.513   -5503.942    2756.252
total_deaths    12.7219      0.389     32.677      0.000      11.954      13.489
new_cases       18.7032      2.924      6.397      0.000      12.940      24.467
new_deaths      11.6549     89.023      0.131      0.896    -163.837     187.147
==============================================================================
Omnibus:                       96.077   Durbin-Watson:                   1.489
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            10028.375
Skew:                          -0.575   Prob(JB):                         0.00
Kurtosis:                      36.516   Cond. No.                     6.91e+03
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.91e+03. This might indicate that there are
strong multicollinearity or other numerical problems.

In [158]:

X = full_data[['total_recovered','new_cases','active_cases']] 
#### fit a OLS model with intercept on total_recovered and  new_cases,active_cases.
y = full_data['total_cases']
X = sm.add_constant(X)
est = sm.OLS(y, X).fit()
print(est.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:            total_cases   R-squared:                       0.999
Model:                            OLS   Adj. R-squared:                  0.999
Method:                 Least Squares   F-statistic:                 1.224e+05
Date:                Fri, 08 May 2020   Prob (F-statistic):               0.00
Time:                        21:50:19   Log-Likelihood:                -1958.1
No. Observations:                 214   AIC:                             3924.
Df Residuals:                     210   BIC:                             3938.
Df Model:                           3                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const             124.7007    164.028      0.760      0.448    -198.652     448.054
total_recovered     1.1000      0.009    124.945      0.000       1.083       1.117
new_cases          -1.0236      0.208     -4.910      0.000      -1.435      -0.613
active_cases        1.0655      0.003    339.454      0.000       1.059       1.072
==============================================================================
Omnibus:                      257.829   Durbin-Watson:                   1.538
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            14047.012
Skew:                           4.961   Prob(JB):                         0.00
Kurtosis:                      41.430   Cond. No.                     7.70e+04
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 7.7e+04. This might indicate that there are
strong multicollinearity or other numerical problems.

World Maps of Total_covid 19 Cases in Countries:¶

Total cases Maps using Plotly ,Mapbox and animation Show how the covid19 Spread in countries.

In [162]:

full_data.fillna(0, inplace=True)

In [163]:

full_data.head(10)

Out[163]:

	country	total_cases	new_cases	total_deaths	new_deaths	total_recovered	active_cases	serious	tot cases/1m_pop	deaths/1m_pop	total_tests	tests/_1m_pop	lat	long
1	USA	1296879	4256	77190	262	218833	1000856	16985	3918.0	233	8364844	25271	37.090240	-95.712891
2	Spain	260117	3262	26299	229	168408	65410	2075	5563.0	562	1932455	41332	40.463667	-3.749220
3	Italy	215858	0	29958	0	96276	89624	1311	3570.0	495	2381288	39385	41.871940	12.567380
4	UK	206715	0	30615	0	0	175756	1559	3045.0	451	1534533	22605	55.378100	3.436000
5	Russia	187859	10699	1723	98	26608	159528	2300	1287.0	12	4980000	34125	61.524010	105.318756
6	France	174791	0	25987	0	55027	93777	2961	2678.0	398	1384633	21213	46.227638	2.213749
7	Germany	169430	0	7392	0	141700	20338	1823	2022.0	88	2755770	32891	51.165691	10.451526
8	Brazil	137309	1616	9307	119	55350	72652	8318	646.0	44	339552	1597	-14.235004	-51.925280
9	Turkey	133721	0	3641	0	82984	47096	1260	1586.0	43	1265119	15000	38.963745	35.243322
10	Iran	104691	1556	6541	55	83837	14313	2711	1246.0	78	558899	6654	32.427908	53.688046

In [166]:

full_data = full_data.sort_values(by=['tests/_1m_pop'])
full_data

Out[166]:

	country	total_cases	new_cases	total_deaths	new_deaths	total_recovered	active_cases	serious	tot cases/1m_pop	deaths/1m_pop	total_tests	tests/_1m_pop	lat	long
107	Diamond Princess	712	0	13	0	645	54	4	0.0	0	0	0	35.414583	139.682033
90	Sudan	930	0	52	0	92	786	0	21.0	1	0	0	12.862807	30.217636
91	Somalia	928	0	44	0	106	778	2	58.0	3	0	0	5.152149	46.199616
94	DRC	897	34	36	0	119	742	0	10.0	0	0	0	4.038300	21.758700
105	Burkina Faso	736	0	48	0	562	126	0	35.0	2	0	0	12.238333	-1.561593
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
57	Bahrain	4404	205	8	0	2027	2369	2	2588.0	5	170719	100330	25.930414	50.637772
196	Falkland Islands	13	0	0	0	13	0	0	3736.0	0	395	113506	51.796300	59.523600
28	UAE	16793	553	174	9	3837	12782	1	1698.0	18	1200000	121330	23.424100	53.847800
76	Iceland	1801	0	10	0	1755	36	0	5278.0	29	51663	151397	64.963051	-19.020835
141	Faeroe Islands	187	0	0	0	186	1	0	3827.0	0	8403	171971	61.892600	6.911800

214 rows × 14 columns

In [171]:

X = covid_india[['Deaths']] 
#### fit a OLS model with intercept on Deaths
y = covid_india['Confirmed']
X = sm.add_constant(X)
est = sm.OLS(y, X).fit()
print(est.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:              Confirmed   R-squared:                       0.890
Model:                            OLS   Adj. R-squared:                  0.890
Method:                 Least Squares   F-statistic:                 1.354e+04
Date:                Fri, 08 May 2020   Prob (F-statistic):               0.00
Time:                        21:57:00   Log-Likelihood:                -12356.
No. Observations:                1671   AIC:                         2.472e+04
Df Residuals:                    1669   BIC:                         2.473e+04
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const        112.9189      9.963     11.334      0.000      93.377     132.461
Deaths        22.6790      0.195    116.342      0.000      22.297      23.061
==============================================================================
Omnibus:                     1165.847   Durbin-Watson:                   1.700
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            30900.445
Skew:                           2.903   Prob(JB):                         0.00
Kurtosis:                      23.251   Cond. No.                         52.9
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

In [173]:

X = covid_india[['Cured']] 
#### fit a OLS model with intercept on Cured Cases
y = covid_india['Confirmed']
X = sm.add_constant(X)
est = sm.OLS(y, X).fit()
print(est.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:              Confirmed   R-squared:                       0.793
Model:                            OLS   Adj. R-squared:                  0.793
Method:                 Least Squares   F-statistic:                     6388.
Date:                Fri, 08 May 2020   Prob (F-statistic):               0.00
Time:                        21:57:18   Log-Likelihood:                -12886.
No. Observations:                1671   AIC:                         2.578e+04
Df Residuals:                    1669   BIC:                         2.579e+04
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const         42.6187     14.004      3.043      0.002      15.151      70.086
Cured          4.2145      0.053     79.925      0.000       4.111       4.318
==============================================================================
Omnibus:                      645.833   Durbin-Watson:                   1.660
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            25483.266
Skew:                           1.103   Prob(JB):                         0.00
Kurtosis:                      22.004   Cond. No.                         281.
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Analysis of Covid_19 Cases in India and its State /Union Territory¶

Analysis Based on Age group in india¶

In [177]:

fig = px.bar(covid_india_age, y='TotalCases', x='AgeGroup', text='Percentage')
fig.update_traces(texttemplate='%{text}', textposition='outside',marker_color='lightsalmon')
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.show()

Analysis of Covid_19 Cases Based on Testing (Positive Cases and Negative Case) in India :¶

In [179]:

fig = px.scatter(covid_india_state_testing, x='Date', y='Positive', title='Positive Cases Time Series with Rangeslider', range_x=['2020-01-23','2020-05-04'],range_y = [1,100000])
fig.update_traces(marker_color='indianred')
fig.update_xaxes(rangeslider_visible=True)
fig.show()
fig1 = px.scatter(covid_india_state_testing, x='Date', y='Negative', title='Negative Cases Time Series with Rangeslider',range_x = ['2020-01-23','2020-05-04'],range_y = [1,1000000])
fig1.update_traces(marker_color='lightsalmon')
fig1.update_xaxes(rangeslider_visible=True)
fig1.show()

	Sno	Date	Time	State/UnionTerritory	ConfirmedIndianNational	Confirmed
0	1	30/01/20	6:00 PM	Kerala	1	1
1	2	31/01/20	6:00 PM	Kerala	1	1
2	3	01/02/20	6:00 PM	Kerala	2	2
3	4	02/02/20	6:00 PM	Kerala	3	3
4	5	03/02/20	6:00 PM	Kerala	3	3

	Sno	AgeGroup	TotalCases	Percentage
0	1	0-9	22	3.18%
1	2	10-19	27	3.90%
2	3	20-29	172	24.86%
3	4	30-39	146	21.10%
4	5	40-49	112	16.18%
5	6	50-59	77	11.13%
6	7	60-69	89	12.86%
7	8	70-79	28	4.05%
8	9	>=80	10	1.45%
9	10	Missing	9	1.30%

	Date	State	TotalSamples	Negative	Positive
0	2020-04-17	Andaman and Nicobar Islands	1403.0	1210.0	12.0
1	2020-04-24	Andaman and Nicobar Islands	2679.0	NaN	27.0
2	2020-04-27	Andaman and Nicobar Islands	2848.0	NaN	33.0
3	2020-05-01	Andaman and Nicobar Islands	3754.0	NaN	33.0
4	2020-04-02	Andhra Pradesh	1800.0	1175.0	132.0
...	...	...	...	...	...
754	2020-04-30	West Bengal	16525.0	NaN	758.0
755	2020-05-01	West Bengal	18566.0	NaN	NaN
756	2020-05-02	West Bengal	20976.0	NaN	795.0
757	2020-05-03	West Bengal	22915.0	NaN	922.0
758	2020-05-04	West Bengal	25116.0	NaN	1259.0

	country	latitude	longitude	name
0	AD	42.546245	1.601554	Andorra
1	AE	23.424076	53.847818	United Arab Emirates
2	AF	33.939110	67.709953	Afghanistan
3	AG	17.060816	-61.796428	Antigua and Barbuda
4	AI	18.220554	-63.068615	Anguilla
...	...	...	...	...
270	MS Zaandam	52.442039	4.829199	MS Zaandam
271	Caribbean Netherlands	12.178400	68.2385	Caribbean Netherlands
272	St. Barth	17.900000	62.8333	St. Barth
273	Saint Pierre Miquelon	46.885200	56.3159	Saint Pierre Miquelon
274	CAR	6.611100	20.9394	CAR

	TotalSamples	Negative	Positive
count	759.000000	623.000000	750.000000
mean	20797.184453	21013.754414	762.126667
std	29959.945068	30047.832110	1542.251749
min	58.000000	0.000000	0.000000
25%	2392.500000	2492.500000	33.000000
50%	8612.000000	8310.000000	207.000000
75%	24910.000000	24462.500000	737.500000
max	175323.000000	162349.000000	14541.000000

	SNo	DateTime	TotalSamplesTested	TotalIndividualsTested	TotalPositiveCases
37	38	23/04/20 9:00	541789.0	525667.0	23502.0
38	39	24/04/20 9:00	579957.0	NaN	NaN
39	40	25/04/20 9:00	625309.0	NaN	NaN
40	41	26/04/20 9:00	665819.0	NaN	NaN
41	42	27/04/20 9:00	716733.0	NaN	NaN

	Country/Region	Date
8295	India	1/23/20
8296	India	1/24/20
8297	India	1/25/20
8298	India	1/26/20
8299	India	1/27/20